The challenge and opportunity of battery lifetime prediction from field data

Summary Accurate battery life prediction is a critical part of the business case for electric vehicles, stationary energy storage, and nascent applications such as aircraft. Existing methods are based on relatively small but well-designed lab datasets controlled test conditions incorporating field data crucial to build complete picture how cells age in real-world situations. This comes with additional challenges because end-use have uncontrolled operating conditions, less accurate sensors, collection storage concerns, infrequent access validation checks. We explore range techniques estimating lifetime from suggest that combining machine learning approaches physical models promising method, enabling inference noisy data, assessment second-life condition, extrapolation future usage conditions. work highlights opportunity insights gained reduce costs improve designs.